Repository that holds data, scripts and figures regarding to Biomining MAGs-SMAG article: "Profiling extremophile bacterial communities recovered from a mining tailing against soil ecosystems through comparative genome-resolved metagenomics and evolutionary analysis".
We classified the obtained set of 44 bacterial MAGs belonging to the S15 sample using GTDB-Tk. Quality metrics with CheckM, and mapping of abundances were also assessed.
See main figure #1.
Also supplementary figure #1, supplementary figure #2.
We analyzed the 44 mining MAGs across non-mining ecosystems (SMAG catalog, bacterial bins), to compare their capacities over intrinsic pathways (copper, iron, and sulfur proteins).
See main figure #2.
Also supplementary figure #3, supplementary figure #4.
We adapted a robust methodology to quantify the dN/dS ratios (non-synonymous and synonymous substitutions) across the mining genes, using the dNdScv package for R. The steps are summarised below:
- Build the reference index with bwa-mem2:
#we index only the binned contigs of S15 sample
bwa-mem2 index S15_bins.fasta
Next, we used alnsl, an in-house Nextflow pipeline which runs bwa-mem2 to align the reference genome to the raw reads and elPrep to analyze the alignment, refine and sort the resulting bam file (via samtools). Example execution:
#prepare reference file for elPrep
elprep fasta-to-elfasta ref.fa ref.fa.elfasta
#run the pipeline (check both config and environment settings)
nextflow -bg run alnsl/main.nf --csv reads.csv -c alnsl/nextflow.config -profile uoh -params-file alnsl/aln-params.yml
Here, the output is a file named S15_bins.bam.
- Call variants with bcftools (uncompressed
VCFformat):
bcftools mpileup -Ov -f S15_bins.fasta S15_bins.bam | bcftools call --ploidy 1 -mv -Ov -o S15.vcf
Now, we have our S15.vcf file.
- Install, configure and run SnpEff to annotate variants:
#step 1: build database (~13 min)
#it generates two binary files for our sample: sequence.bin and snpEffectPredictor.bin
java -Xmx16g -jar snpEff.jar build -config snpEff.config -dataDir data/ -noCheckCds -noCheckProtein -gff3 -v S15_bins &> data/S15_bins.build.log
#step 2: annotate (~3 min)
java -Xmx16g -jar snpEff.jar -stats S15.ann.html S15_bins S15.vcf > S15.ann.vcf
- Install and run dNdScv to calculate the dN/dS ratios:
#first, we parse the .gff file in a tab-delimited CDS table with gene coordinates
python3 parse_cds.py genes.gff > cds_table.txt
#call buildref (very slow: ~2 hrs in building object)
#here we need the previous .fasta file
buildref(cdsfile = "dndscv/buildref/cds_table.txt",
genomefile = "dndscv/buildref/S15_bins.fa",
outfile = "dndscv/buildref/S15_refcds.rda")
#then, we use the annotated S15.ann.vcf file to extract mutations
python3 parse_snpeff.py S15.ann.vcf > S15_mutations.txt
#run dndscv (runtime ~8 min, high memory usage) and save object
#this object is later used to calculate dN/dS ratios for each annotated gene or CDS (post-analysis)
snp_dndsout <- dndscv(snp_mutations, refdb = "dndscv/buildref/S15_refcds.rda",
max_muts_per_gene_per_sample = Inf, max_coding_muts_per_sample = Inf,
cv = NULL, outmats = TRUE)
save(snp_dndsout, file = "dndscv/run/S15_dndsout.rda")
#output objects:
buildref: S15_refcds.rda (118.8 Mb)
output: S15_dndsout.rda (545.9 Mb)
See main figure #3, main figure #4.
Also supplementary figure #5.
See supplementary tables.
|- data/
| |- dndscv/
| | |- cds_table.txt
| | |- parse_cds.py
| | |- parse_snpeff.py
| | |- S15_mutations.txt
| |- MAG_SMAG/
| | |- mags_rel_abundances.txt
| | |- mags_S15_r214.txt
| | |- smag_data.tsv
| | |- smag_filtered_bins.tsv
| | |- gtdbtk.DASTool-S15.tree
|- figures/
| |- fig_1.png
| |- fig_2.png
| |- fig_3.png
| |- fig_4.png
| |- fig_s1.png
| |- fig_s2.png
| |- fig_s3.png
| |- fig_s4.png
| |- fig_s5.png
|- R/
| |- paper_filter_metadata.Rmd
| |- paper.Rmd
| |- paper.RData
Moises A. Rojas, Gladis Serrano, Jorge Torres, Jaime Ortega, Gabriel Gálvez, Emilio Vilches, Valentina Parra, Angélica Reyes-Jara, Vinicius Maracaja-Coutinho, Lorena Pizarro, Mauricio Latorre, Alex Di Genova; "Profiling extremophile bacterial communities recovered from a mining tailing against soil ecosystems through comparative genome-resolved metagenomics and evolutionary analysis", bioRxiv, Aug 2024, doi: 10.1101/2024.08.28.610100.
- Dec 2024: Environmental Microbiome (in revision).